Authors:
Saeed Mirghasemi
;
Ramesh Rayudu
and
Mengjie Zhang
Affiliation:
Victoria University of Wellington, New Zealand
Keyword(s):
Noisy Image Segmentation, Fuzzy C-Means, Particle Swarm Optimization, Impulse Noise.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Soft Computing
;
Swarm/Collective Intelligence
Abstract:
Introducing methods that can work out the problem of noisy image segmentation is necessary for real-world
vision problems. This paper proposes a new computational algorithm for segmentation of gray images contaminated
with impulse noise. We have used Fuzzy C-Means (FCM) in fusion with Particle Swarm Optimization
(PSO) to define a new similarity metric based on combining different intensity-based neighborhood features.
PSO as a computational search algorithm, looks for an optimum similarity metric, and FCM as a clustering
technique, helps to verify the similarity metric goodness. The proposed method has no parameters to tune, and
works adaptively to eliminate impulsive noise. We have tested our algorithm on different synthetic and real
images, and provided quantitative evaluation to measure effectiveness. The results show that, the method has
promising performance in comparison with other existing methods in cases where images have been corrupted
with a high density noise.